# Podcast with Microsoft's Vincent van Wingerden

My guest today is Vincent van Wingerden, Technical Architect for Data & AI at Microsoft. Vincent and I spoke about quantum-inspired computing, about the open-source Quantum Katas education project and much more.

Listen to additional episodes by selecting 'podcasts' on our Insights page

## THE FULL TRANSCRIPT IS BELOW

**Yuval Boger (Classiq)**: Hello, Vincent. And thanks for joining me today.

**Vincent va wingerden (Microsoft)**: Hello, Yuval. I'm very happy to be here.

**Yuval**: Glad to have you. So who are you and what do you do?

**Vincent**: So my name is Vincent van Wingerden. I am working at the Dutch subsidiary at Microsoft, and I am keeping myself busy with the data and AI space. And I work a lot in the quantum space and quantum education and talking to our customers and our partners about what quantum will bring and how a quantum computer will basically change our lives forever once it's here, but also the impact which quantum will have today and what we can achieve with quantum algorithms today.

**Yuval**: Do you feel that you have to go to customers and start explaining what quantum is and convince them to think about quantum, or are most of the conversations initiated by customers coming to you and saying, "We hear about quantum, and we want to get into it"?

**Vincent**: I think both types of customers are there. So there are customers who are really at the forefront. And I think these are also customers who already, who will be there and have the most impact right away. For example, there is, like chemistry will be an industry, which will be heavily changed if we have a quantum computer available. And I think these customers are already more proactively seeking towards these solutions, while maybe other customers who are not aware how much a quantum computer will change their world or will change their perspective, are maybe not as proactive to coming towards us or coming towards anybody else. And we come to them.

I think a good example will be, for example, the logistics companies. So, for example, if we're talking about transportation and optimizing routes, and how do we get a package from A to B as efficiently as possible, that is not always seen as a solution where a quantum computer will make a huge difference. However, that is really a space where a quantum computer could make a difference or even where quantum-inspired algorithms, which we can execute today, on classical hardware can already make such a huge difference. And so I think both these types of companies exist. So either companies who we proactively go towards and customers who are coming towards us.

**Yuval**: You mentioned quantum-inspired computing, and I saw that on your blog, but I'm not sure I heard that many other places. Could you explain what that is?

**Vincent**: Yeah, sure. We have been researching quantum mechanics and quantum concepts for a long time already in science. And we sort of understand how everything works. And to make it simple, quantum-inspired basically takes these principles of quantum mechanics and quantum computing but executes them on classical hardware. And that can be, for example, on CPUs on very large clusters, or that can be on FPGAs, and some things, these can have lots of advantages in specific areas. For example, if we're talking about optimization, let's take it again. Let's go back to the previous example that we have, logistics. It's quite a hard subject to solve. It's quite hard to get an optimal route, especially if you have a large fleet of cars. If you have many addresses that you want to visit, there's a lot of variables going on in the search space for an optimal route, for an optimal delivery schedule is getting very big.

And these types of problems, they can be solved with classical algorithms, but mostly, solving these can take so much time that basically takes too much time to find an optimal route or an optimal schedule, and therefore what we do is we try to get the best possible schedule given a specific amount of time. And that yields not always the ideal route or the ideal schedule. Now, what can a quantum-inspired computer do? We can take these concepts that we've learned from quantum mechanics, from quantum computing, and we take them and execute them on classical hardware. And we can basically improve on those schedules. So, make more efficient schedules while you are creating schedules right now, or use less time and create as efficient schedules as we have right now. A nice example, which is a public reference case from Microsoft, is, for example, using this technology to create MRI scans.

So, MRI scans are basically done in a hospital to scan a specific joint or scan your brain or anything like that. And to create an optimal path for a scanner, we must do some calculations and we must do some math. And using these optimization techniques from quantum-inspired, we can basically either create more efficient paths of scanning, given the same amount of time to solve the problem, or we can create as good as paths that we have right now, but in a lot lesser, in less time, basically. So quantum-inspired is really an area where we have advantages of the learnings that we have from quantum mechanics and quantum computing right now. And that is also something that you can get started with right now. So you can be a Python developer and made use of these awesome algorithms and solvers right now.

**Yuval**: Is that sometimes called digital annealing? Is that basically taking quantum annealing concepts and running them on a digital computer?

**Vincent**: Yeah. So it has the same purpose as a quantum annealer, yep.

**Yuval**: Now, one of the promises of quantum computers is they can solve much more complex multi-dimensional problems than classical computers. But if you take quantum-inspired concepts and run them on classical computers, aren't you running into the same limitations that quantum computers were supposed to solve, that the problems become too complex for classical computers?

Vincent: Yeah. So, of course, a quantum-inspired computer at the moment is not as powerful or will be as powerful as a quantum computer eventually will be. So eventually, if we have this large-scale, fault-tolerant quantum computer, that will be way more efficient than the quantum-inspired solvers that we have right now. However, we don't have that large-scale fault-tolerant quantum computer at the moment. So, if you want to take advantage of the knowledge that we have for quantum mechanics and quantum computers at the moment, this is a great tool to use. And also, given that it will take quite a lot of time until we have this fault-tolerant, large-scale quantum computer, this is a great step in between. And also to prepare people and prepare companies on what this step will be, get people into the quantum realm and understand how solvers, how these new type of solvers can already bring huge benefits in contrast to the solvers that they are using right now.

**Yuval**: You mentioned that Python developers could start using these optimizations today, but thinking ahead, do you expect people to need to be retrained either conceptually or sort of it's just a little bit different programming language, to use the advantages of quantum computing? I know you're involved in an education project for that.

**Vincent**: Yeah, so I'm working on a project called Quantum Katas, so it's an open-source project on GitHub. If you go to github.com/microsoft/quantumkatas, you'll find great learning resources. So, to give you a little bit of background on this project and what it is. Basically, the Quantum Katas is a set of exercises to get people from zero to hero in quantum computing. So we basically completely start at the beginning. So we're talking about linear algebra and how to get started with that. That is all done in Python, the math part. So basically, what we're doing is, we're giving people exercises to get them familiar with specific concepts, and that starts with matrix multiplication and the adjoint of a matrix, you name it. So really get them started with the basics of the math, which is involved in quantum computing. Then we scale up and start talking about actual Qubits. How does a Qubit work? What can you do with a Qubit? And that is not done in Python anymore.

So, then we're switching to a language, which is called Q#. This is a domain-specific language from Microsoft, and it is domain-specific, so it's specifically designed to program quantum computers. So it is not really used for classical programming. And this Q# language, basically the reason why Microsoft has started building a Q# language, instead of, for example, creating a Python library, which is also an option, is because the programming for a quantum computer is so vastly different than programming for a regular computer, so for a girth computer, that basically, we want to give this or give all the possibilities that a quantum computer offers in this new programming language. And the idea is that you have one language, Q#, and that can target multiple hardware architectures. So currently, for example, if you start writing code in Q#, then you can target an IonQ machine as well as a Honeywell machine in Azure Quantum.

So that is the language that we are using in the Quantum Katas. So people will start with Python to learn the math concepts, and then people will move on to Q#. And from that Q# language, people will really ramp up. So, start with basic concepts on what a qubit is, then go to more advanced concepts. So how to use gates, how to do measurements, how to do joint measurements, you name it, multiple Qubits, and eventually really move on to algorithm design. So how does the Grover algorithm work? How does the Shor algorithm work? And really from there go on to more and more advanced subjects.

So it is a series of programming exercises, basically to get people going in a quantum computer and teach them how specific algorithms work and to teach them how it works, we basically ask people to write that algorithm in Q#, of course, it's step-by-step, so it's not “go write Grover's algorithm” from zero, but start with the small steps, start building a small oracle, and then using Grover search to, for example, find some optimal solution or find their solution using Grover search.

So that is really the idea of the Katas, to get people into quantum computing, to teach people how to program this quantum computer using Q#. And this project is started by a colleague of mine, called Maria. And she is working on this project for quite a while already. I've been coming on board roughly two years ago, and she's not just working on the Quantum Katas, but she's also working on some other really cool concepts to really teach people how to program these quantum computers. And I also think that that may be a nice guest in the future. But to come back to the Katas and to come back to your question on if everybody should change and go from a classical developer towards a quantum developer, I think eventually you don't need to know how to program gates or how to really program circuits.

Like now, at the moment, if you're programming for a classical computer, you do not need to program an assembly code. You do not need to program all the gates that you're going to use. And that's also what Microsoft is doing with Q#. We're trying to really add high-level library functionality, also to specific domains within quantum. So, for example, chemistry or machine learning. So people do not need to use all those gates and do all the difficult, low-level stuff, but can use more high-level library functions. So people really do eventually need to change, of course, how they work, but they don't need to know, at a gate level, what they actually should do.

And I think once we get more closer towards these larger-scale, fault-tolerant quantum computers, that it will get a little bit easier for developers to develop quantum programs. But of course, you will need to learn how to use these new libraries and understand what a quantum computer does to really take full advantage of the things that a quantum computer has to offer, just like once GPU's game out. And I think that's a good comparison to a quantum computer. You really had to learn how to use a GPU to accelerate specific workloads. I think the same will happen with a quantum computer. You will need to know how to use this accelerator to accelerate your workloads, whether it's chemistry or logistics, or anything you really need to get into that. And one great tool to get into it, to learn about how quantum computer works, is this Quantum Katas project that we're building.

**Yuval**: What would be your preference for background for someone who wants to get into quantum? Let's assume you're advising a company, and that company says, "Yes, we want to start a quantum team. We want to hire or repurpose three people to do that." Do you prefer some open-minded excellent programmer that does Python, or do you prefer a Ph.D. in quantum information systems or sort of a physics major as opposed to a computer science one?

Vincent: Well, I think it's a difficult question for me to answer because I have none of those backgrounds. I studied economics. But I think being open-minded is very important in this space also because we see so much change at the moment, and we see so much happening in the quantum realm. So if you can pick three people, it will probably be a combination of those. A great programmer who wants to learn a new language like Q# or wants to learn a library of Python, which really can program a quantum computer, but it is very good to have a very well understanding of how the quantum mechanics work under the hood.

So having somebody who really understands what a quantum computer can do and how to leverage algorithms, and how to actually build a quantum algorithm, I think that is something that currently PhDs in quantum mechanics, for example, will be easier, will be more suitable may be to do, than a programmer at the moment. So, a combination of them both, I think, will be perfect. And I think in the current state that quantum is in, both professions can become quantum programmers. I mean whether, regardless of your background, the number of tutorials, the amount of knowledge that is available on the internet is so big that either of those can become great quantum programmers.

**Yuval**: And thinking a little bit into the future as we move from academia to production, let's take a two or three-year timeframe. What are the applications that you're most excited about that could deliver true business value on quantum in that timeframe?

**Vincent**: Yeah, so I think in the timeframe of two or three years, I mean, a lot will happen, right? It will be awesome to get some bigger quantum computers. However, they probably won't be full-scale, fault-tolerant yet. So we have to get some sort of algorithms that allow for a little bit of error, and we see those pop up more and more. These algorithms allow error and where we can still get the value of that quantum computer. However, I also believe that this will be, I think it will be hard to really get large applications, which everybody's waiting for, for example, Shor's algorithm, to run within that timeframe.

So I think that timeframe of two or three years will also be still a lot of academia, still getting a lot of people into the field, making sure we are ready once these large scale quantum computers will be ready, but also is quantum-inspired. How can we sort of use this time and use this step in between to leverage that technology before the big step will come? And once this big style of a quantum computer will come, of course, everybody's waiting for Shor's algorithm to really light up and start breaking the first keys. But I think before that is ready, we still have some time to go.

**Yuval**: Understood. I must ask you, looking at your background, you mentioned economics, and now you're doing quantum, but you're quite the traveler. What's the most exotic location that you visited?

**Vincent**: Yeah. I really like to travel in my free time, and I think the most exotic place at the moment which we visited was Greenland, where I did a hiking trip of roughly 10 days, where I walked, basically in the middle of nowhere for 10 days, just with me, my backpack and some food, and saw some awesome wildlife and those type of things. So yeah, I think that or maybe Alaska where we also got dropped in the middle of nowhere, and just saw, was us and some bears, and then that's it.

**Yuval**: That's amazing. Thinking about all these multiple places, in the quantum sense, that brings up the traveling salesperson or the traveling hiker problem. Given today's capacity, how large of a TSP problem do you feel that computers can solve today?

**Vincent**: Do you mean quantum computers or traditional computers?

**Yuval**: I was thinking about quantum computers, but I'm happy to have you answer both sides.

**Vincent**: Let's start with quantum computers then. So the biggest, so TSP problem, so traveling salesman problem, it doesn't get that far. You really need some Qubits to encode all the information on all the possible routes. So to get that, I think it will not be... It will be quite small. So, it will be ten or maybe 20 locations, which is not very big. So a traditional computer would be able to solve that. However, going towards quantum-inspired, I think that we can go already, a little bit bigger, so I wrote a blog post on how to solve, I think it was 50 locations on quantum-inspired, to be honest, to give a little bit of a disclaimer there, this is not the most efficient way to use quantum-inspired, but it is I think the easiest way to show value.

So, I think that currently we can do more with classical computing, but eventually this will really be a game changer also because if we can, let's say you can create 10 to 15% more efficient routes, imagine how much kilometers less a car has to drive or a boat has to sail, and that can have huge impact on our environment.

**Yuval**: And the last question on TSP, as we get close to the end of our conversation, how long does it take to get that solution for a 50 stop TSP on quantum-inspired computing?

**Vincent**: I hope I wrote that in my blog post, but on top of my head, I think we had something like 20 seconds if I'm not mistaken. But I have to look it up, but it was not by far the most efficient implementation. So it could be way more efficient, but I think it was a good a way to show people on how to use quantum-inspired computing.

**Yuval**: I think so too. So Vincent, how can people get in touch with you to learn more about Quantum Katas and about the other work that you're doing?

**Vincent**: Yeah. So I have a website, a blog with some posts, so the TSP problem and some other things, it's called a Vincent.frl. So that's one thing, but the best way I think to get in touch with me is via LinkedIn. So LinkedIn, Vincent van Wingerden, so probably also the name will be in the title of the podcast. So that will be the easiest way to get into contact with me.

**Yuval**: Excellent. Thank you very much for joining me today, Vincent.

**Vincent**: Of course, you're very welcome.

My guest today is Vincent van Wingerden, Technical Architect for Data & AI at Microsoft. Vincent and I spoke about quantum-inspired computing, about the open-source Quantum Katas education project and much more.

Listen to additional episodes by selecting 'podcasts' on our Insights page

## THE FULL TRANSCRIPT IS BELOW

**Yuval Boger (Classiq)**: Hello, Vincent. And thanks for joining me today.

**Vincent va wingerden (Microsoft)**: Hello, Yuval. I'm very happy to be here.

**Yuval**: Glad to have you. So who are you and what do you do?

**Vincent**: So my name is Vincent van Wingerden. I am working at the Dutch subsidiary at Microsoft, and I am keeping myself busy with the data and AI space. And I work a lot in the quantum space and quantum education and talking to our customers and our partners about what quantum will bring and how a quantum computer will basically change our lives forever once it's here, but also the impact which quantum will have today and what we can achieve with quantum algorithms today.

**Yuval**: Do you feel that you have to go to customers and start explaining what quantum is and convince them to think about quantum, or are most of the conversations initiated by customers coming to you and saying, "We hear about quantum, and we want to get into it"?

**Vincent**: I think both types of customers are there. So there are customers who are really at the forefront. And I think these are also customers who already, who will be there and have the most impact right away. For example, there is, like chemistry will be an industry, which will be heavily changed if we have a quantum computer available. And I think these customers are already more proactively seeking towards these solutions, while maybe other customers who are not aware how much a quantum computer will change their world or will change their perspective, are maybe not as proactive to coming towards us or coming towards anybody else. And we come to them.

I think a good example will be, for example, the logistics companies. So, for example, if we're talking about transportation and optimizing routes, and how do we get a package from A to B as efficiently as possible, that is not always seen as a solution where a quantum computer will make a huge difference. However, that is really a space where a quantum computer could make a difference or even where quantum-inspired algorithms, which we can execute today, on classical hardware can already make such a huge difference. And so I think both these types of companies exist. So either companies who we proactively go towards and customers who are coming towards us.

**Yuval**: You mentioned quantum-inspired computing, and I saw that on your blog, but I'm not sure I heard that many other places. Could you explain what that is?

**Vincent**: Yeah, sure. We have been researching quantum mechanics and quantum concepts for a long time already in science. And we sort of understand how everything works. And to make it simple, quantum-inspired basically takes these principles of quantum mechanics and quantum computing but executes them on classical hardware. And that can be, for example, on CPUs on very large clusters, or that can be on FPGAs, and some things, these can have lots of advantages in specific areas. For example, if we're talking about optimization, let's take it again. Let's go back to the previous example that we have, logistics. It's quite a hard subject to solve. It's quite hard to get an optimal route, especially if you have a large fleet of cars. If you have many addresses that you want to visit, there's a lot of variables going on in the search space for an optimal route, for an optimal delivery schedule is getting very big.

And these types of problems, they can be solved with classical algorithms, but mostly, solving these can take so much time that basically takes too much time to find an optimal route or an optimal schedule, and therefore what we do is we try to get the best possible schedule given a specific amount of time. And that yields not always the ideal route or the ideal schedule. Now, what can a quantum-inspired computer do? We can take these concepts that we've learned from quantum mechanics, from quantum computing, and we take them and execute them on classical hardware. And we can basically improve on those schedules. So, make more efficient schedules while you are creating schedules right now, or use less time and create as efficient schedules as we have right now. A nice example, which is a public reference case from Microsoft, is, for example, using this technology to create MRI scans.

So, MRI scans are basically done in a hospital to scan a specific joint or scan your brain or anything like that. And to create an optimal path for a scanner, we must do some calculations and we must do some math. And using these optimization techniques from quantum-inspired, we can basically either create more efficient paths of scanning, given the same amount of time to solve the problem, or we can create as good as paths that we have right now, but in a lot lesser, in less time, basically. So quantum-inspired is really an area where we have advantages of the learnings that we have from quantum mechanics and quantum computing right now. And that is also something that you can get started with right now. So you can be a Python developer and made use of these awesome algorithms and solvers right now.

**Yuval**: Is that sometimes called digital annealing? Is that basically taking quantum annealing concepts and running them on a digital computer?

**Vincent**: Yeah. So it has the same purpose as a quantum annealer, yep.

**Yuval**: Now, one of the promises of quantum computers is they can solve much more complex multi-dimensional problems than classical computers. But if you take quantum-inspired concepts and run them on classical computers, aren't you running into the same limitations that quantum computers were supposed to solve, that the problems become too complex for classical computers?

Vincent: Yeah. So, of course, a quantum-inspired computer at the moment is not as powerful or will be as powerful as a quantum computer eventually will be. So eventually, if we have this large-scale, fault-tolerant quantum computer, that will be way more efficient than the quantum-inspired solvers that we have right now. However, we don't have that large-scale fault-tolerant quantum computer at the moment. So, if you want to take advantage of the knowledge that we have for quantum mechanics and quantum computers at the moment, this is a great tool to use. And also, given that it will take quite a lot of time until we have this fault-tolerant, large-scale quantum computer, this is a great step in between. And also to prepare people and prepare companies on what this step will be, get people into the quantum realm and understand how solvers, how these new type of solvers can already bring huge benefits in contrast to the solvers that they are using right now.

**Yuval**: You mentioned that Python developers could start using these optimizations today, but thinking ahead, do you expect people to need to be retrained either conceptually or sort of it's just a little bit different programming language, to use the advantages of quantum computing? I know you're involved in an education project for that.

**Vincent**: Yeah, so I'm working on a project called Quantum Katas, so it's an open-source project on GitHub. If you go to github.com/microsoft/quantumkatas, you'll find great learning resources. So, to give you a little bit of background on this project and what it is. Basically, the Quantum Katas is a set of exercises to get people from zero to hero in quantum computing. So we basically completely start at the beginning. So we're talking about linear algebra and how to get started with that. That is all done in Python, the math part. So basically, what we're doing is, we're giving people exercises to get them familiar with specific concepts, and that starts with matrix multiplication and the adjoint of a matrix, you name it. So really get them started with the basics of the math, which is involved in quantum computing. Then we scale up and start talking about actual Qubits. How does a Qubit work? What can you do with a Qubit? And that is not done in Python anymore.

So, then we're switching to a language, which is called Q#. This is a domain-specific language from Microsoft, and it is domain-specific, so it's specifically designed to program quantum computers. So it is not really used for classical programming. And this Q# language, basically the reason why Microsoft has started building a Q# language, instead of, for example, creating a Python library, which is also an option, is because the programming for a quantum computer is so vastly different than programming for a regular computer, so for a girth computer, that basically, we want to give this or give all the possibilities that a quantum computer offers in this new programming language. And the idea is that you have one language, Q#, and that can target multiple hardware architectures. So currently, for example, if you start writing code in Q#, then you can target an IonQ machine as well as a Honeywell machine in Azure Quantum.

So that is the language that we are using in the Quantum Katas. So people will start with Python to learn the math concepts, and then people will move on to Q#. And from that Q# language, people will really ramp up. So, start with basic concepts on what a qubit is, then go to more advanced concepts. So how to use gates, how to do measurements, how to do joint measurements, you name it, multiple Qubits, and eventually really move on to algorithm design. So how does the Grover algorithm work? How does the Shor algorithm work? And really from there go on to more and more advanced subjects.

So it is a series of programming exercises, basically to get people going in a quantum computer and teach them how specific algorithms work and to teach them how it works, we basically ask people to write that algorithm in Q#, of course, it's step-by-step, so it's not “go write Grover's algorithm” from zero, but start with the small steps, start building a small oracle, and then using Grover search to, for example, find some optimal solution or find their solution using Grover search.

So that is really the idea of the Katas, to get people into quantum computing, to teach people how to program this quantum computer using Q#. And this project is started by a colleague of mine, called Maria. And she is working on this project for quite a while already. I've been coming on board roughly two years ago, and she's not just working on the Quantum Katas, but she's also working on some other really cool concepts to really teach people how to program these quantum computers. And I also think that that may be a nice guest in the future. But to come back to the Katas and to come back to your question on if everybody should change and go from a classical developer towards a quantum developer, I think eventually you don't need to know how to program gates or how to really program circuits.

Like now, at the moment, if you're programming for a classical computer, you do not need to program an assembly code. You do not need to program all the gates that you're going to use. And that's also what Microsoft is doing with Q#. We're trying to really add high-level library functionality, also to specific domains within quantum. So, for example, chemistry or machine learning. So people do not need to use all those gates and do all the difficult, low-level stuff, but can use more high-level library functions. So people really do eventually need to change, of course, how they work, but they don't need to know, at a gate level, what they actually should do.

And I think once we get more closer towards these larger-scale, fault-tolerant quantum computers, that it will get a little bit easier for developers to develop quantum programs. But of course, you will need to learn how to use these new libraries and understand what a quantum computer does to really take full advantage of the things that a quantum computer has to offer, just like once GPU's game out. And I think that's a good comparison to a quantum computer. You really had to learn how to use a GPU to accelerate specific workloads. I think the same will happen with a quantum computer. You will need to know how to use this accelerator to accelerate your workloads, whether it's chemistry or logistics, or anything you really need to get into that. And one great tool to get into it, to learn about how quantum computer works, is this Quantum Katas project that we're building.

**Yuval**: What would be your preference for background for someone who wants to get into quantum? Let's assume you're advising a company, and that company says, "Yes, we want to start a quantum team. We want to hire or repurpose three people to do that." Do you prefer some open-minded excellent programmer that does Python, or do you prefer a Ph.D. in quantum information systems or sort of a physics major as opposed to a computer science one?

Vincent: Well, I think it's a difficult question for me to answer because I have none of those backgrounds. I studied economics. But I think being open-minded is very important in this space also because we see so much change at the moment, and we see so much happening in the quantum realm. So if you can pick three people, it will probably be a combination of those. A great programmer who wants to learn a new language like Q# or wants to learn a library of Python, which really can program a quantum computer, but it is very good to have a very well understanding of how the quantum mechanics work under the hood.

So having somebody who really understands what a quantum computer can do and how to leverage algorithms, and how to actually build a quantum algorithm, I think that is something that currently PhDs in quantum mechanics, for example, will be easier, will be more suitable may be to do, than a programmer at the moment. So, a combination of them both, I think, will be perfect. And I think in the current state that quantum is in, both professions can become quantum programmers. I mean whether, regardless of your background, the number of tutorials, the amount of knowledge that is available on the internet is so big that either of those can become great quantum programmers.

**Yuval**: And thinking a little bit into the future as we move from academia to production, let's take a two or three-year timeframe. What are the applications that you're most excited about that could deliver true business value on quantum in that timeframe?

**Vincent**: Yeah, so I think in the timeframe of two or three years, I mean, a lot will happen, right? It will be awesome to get some bigger quantum computers. However, they probably won't be full-scale, fault-tolerant yet. So we have to get some sort of algorithms that allow for a little bit of error, and we see those pop up more and more. These algorithms allow error and where we can still get the value of that quantum computer. However, I also believe that this will be, I think it will be hard to really get large applications, which everybody's waiting for, for example, Shor's algorithm, to run within that timeframe.

So I think that timeframe of two or three years will also be still a lot of academia, still getting a lot of people into the field, making sure we are ready once these large scale quantum computers will be ready, but also is quantum-inspired. How can we sort of use this time and use this step in between to leverage that technology before the big step will come? And once this big style of a quantum computer will come, of course, everybody's waiting for Shor's algorithm to really light up and start breaking the first keys. But I think before that is ready, we still have some time to go.

**Yuval**: Understood. I must ask you, looking at your background, you mentioned economics, and now you're doing quantum, but you're quite the traveler. What's the most exotic location that you visited?

**Vincent**: Yeah. I really like to travel in my free time, and I think the most exotic place at the moment which we visited was Greenland, where I did a hiking trip of roughly 10 days, where I walked, basically in the middle of nowhere for 10 days, just with me, my backpack and some food, and saw some awesome wildlife and those type of things. So yeah, I think that or maybe Alaska where we also got dropped in the middle of nowhere, and just saw, was us and some bears, and then that's it.

**Yuval**: That's amazing. Thinking about all these multiple places, in the quantum sense, that brings up the traveling salesperson or the traveling hiker problem. Given today's capacity, how large of a TSP problem do you feel that computers can solve today?

**Vincent**: Do you mean quantum computers or traditional computers?

**Yuval**: I was thinking about quantum computers, but I'm happy to have you answer both sides.

**Vincent**: Let's start with quantum computers then. So the biggest, so TSP problem, so traveling salesman problem, it doesn't get that far. You really need some Qubits to encode all the information on all the possible routes. So to get that, I think it will not be... It will be quite small. So, it will be ten or maybe 20 locations, which is not very big. So a traditional computer would be able to solve that. However, going towards quantum-inspired, I think that we can go already, a little bit bigger, so I wrote a blog post on how to solve, I think it was 50 locations on quantum-inspired, to be honest, to give a little bit of a disclaimer there, this is not the most efficient way to use quantum-inspired, but it is I think the easiest way to show value.

So, I think that currently we can do more with classical computing, but eventually this will really be a game changer also because if we can, let's say you can create 10 to 15% more efficient routes, imagine how much kilometers less a car has to drive or a boat has to sail, and that can have huge impact on our environment.

**Yuval**: And the last question on TSP, as we get close to the end of our conversation, how long does it take to get that solution for a 50 stop TSP on quantum-inspired computing?

**Vincent**: I hope I wrote that in my blog post, but on top of my head, I think we had something like 20 seconds if I'm not mistaken. But I have to look it up, but it was not by far the most efficient implementation. So it could be way more efficient, but I think it was a good a way to show people on how to use quantum-inspired computing.

**Yuval**: I think so too. So Vincent, how can people get in touch with you to learn more about Quantum Katas and about the other work that you're doing?

**Vincent**: Yeah. So I have a website, a blog with some posts, so the TSP problem and some other things, it's called a Vincent.frl. So that's one thing, but the best way I think to get in touch with me is via LinkedIn. So LinkedIn, Vincent van Wingerden, so probably also the name will be in the title of the podcast. So that will be the easiest way to get into contact with me.

**Yuval**: Excellent. Thank you very much for joining me today, Vincent.

**Vincent**: Of course, you're very welcome.